Age-Hardening Response of AlMgZn Alloys with Cu and Ag Additions

Stemper, Lukas; Tunes, Matheus A.; Oberhauser, Paul; Uggowitzer, Peter J.; Pogatscher, Stefan

doi:10.2139/ssrn.3559983

“…This section mainly discusses the validity of sample data transfer aided process optimization, as well as the importance of sample According to traditional research ideas, abundant experiments are necessary to establish the relationship between the processing and properties of a new alloy and then determine a reasonable process through analysis to design and optimize the process of the alloy. The alloy composition and processing are key factors affecting the properties of precipitation-strengthened aluminum alloys [6][7][8]11,14,21,27,28 , and there are interactions between the composition, solution treatment, and aging treatment 6,27,28 .…”

Section: Sample Data Transfer Aided Process Optimizationmentioning

confidence: 99%

A rapid and effective method for alloy materials design via sample data transfer machine learning

Jiang

¹

,

Zhang

²

,

Hu

³

et al. 2023

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One of the challenges in material design is to rapidly develop new materials or improve the performance of materials by utilizing the data and knowledge of existing materials. Here, a rapid and effective method of alloy material design via data transfer learning is proposed to efficiently design new alloys using existing data. A new type of aluminum alloy (E2 alloy) with ultra strength and high toughness previously developed by the authors is used as an example. An optimal three-stage solution-aging treatment process (T66R) was efficiently designed transferring 1053 pieces of process-property relationship data of existing AA7xxx commercial aluminum alloys. It realizes the substantial improvement of strength and plasticity of E2 alloy simultaneously, which is of great significance for lightweight of high-end equipment. Meanwhile, the microstructure analysis clarifies the mechanism of alloy performance improvement. This study shows that transferring the existing alloy data is an effective method to design new alloys.

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“…According to traditional research ideas, abundant experiments are necessary to establish the relationship between the processing and properties of a new alloy and then determine a reasonable process through analysis to design and optimize the process of the alloy. The alloy composition and processing are key factors affecting the properties of precipitation-strengthened aluminum alloys [6][7][8]11,14,21,27,28], and there are interactions between the composition, solution treatment, and aging treatment [6,27,28].…”

Section: Sample Data Transfer Aided Process Optimizationmentioning

confidence: 99%

A Novel Method for Alloy Materials Design via Sample Data Transfer Machine Learning

Xie

¹

,

Jiang

²

,

Zhang

³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

One of the challenges in material design is to rapidly develop new materials or improve the performance of materials by utilizing the data and knowledge of existing materials. Here, a novel method of alloy material design via data transfer learning is proposed to efficiently design new alloys using existing data. A new type of aluminum alloy with ultra strength and high toughness (so-called E2 alloy) previously developed by the authors is used as an example, and an optimal three-stage solution-aging treatment process (which is very difficult to design by trial-and-error method, T66R process for short) was rapidly designed based on 1053 pieces of heat treatment process data of AA7xxx series commercial high-strength aluminum alloy and a total of 29 groups of experimental data of E2 alloy. It overcomes the bottleneck of expensive theoretical calculations and more than millions of possible process combinations in the experimental trial-and-error space in the process design of complex alloy heat treatment. Compared with the common T6 process in the aluminum industry, the T66R process can increase the ultimate tensile strength and elongation of the E2 alloy simultaneously, from 715 ± 6 MPa and 8.4 ± 0.4% to 767 ± 6 MPa and 13.4 ± 0.5%, respectively. The results of microstructure characterization indicate that employing the optimal T66R process, the amount of micron-scale insoluble phases in the E2 alloy is sharply reduced, and the number of nano-precipitates is significantly increased, which leads to the simultaneous improvement of the strength and plasticity of the alloy. This study shows that transferring the existing alloy data is an effective method to design new alloys.

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“…The evolution of physical metallurgy as the main branch of science addressing the relationship between microstructure, properties and processing of metals and alloys (and later of materials science, addressing all classes of materials) has brought -over the past century -unprecedented advances in several fields of human technology. Lightweight Al-, Mg-and Ti-based alloys entirely revolutionised modern industry with applications ranging from recyclable and high strength-to-weight ratio parts for both automotive and aerospace industries to the infant industry of biocompatible and biodegradable implants [1][2][3][4][5][6][7][8][9][10][11][12][13]. Fe-based alloys -in particular steels -have formed the backbone of our technology thus far [14,15] and are still just as relevant in modern technology as structural materials.…”

Section: Introductionmentioning

confidence: 99%